The present exemplary embodiments relate to methods and systems for improving sharpness of halftoned images. Embodiments find particular application in conjunction with document processing systems and will be described with particular reference thereto. However, it is to be appreciated that some embodiments are amenable to other applications.
It is sometimes desirable to enhance the sharpness of an image. For example, an available document may be the result of many generations of photocopying or may have become diffuse with age or mishandling. Or, it may simply be that a user of an image may consider an image to be unsuitably sharp as a matter of aesthetic preference. Nevertheless, there may be a need or desire to use a copy of the image for some purpose such as, for example, for inclusion in a publication, for archival purposes or for investigative or evidentiary purposes. For instance, a portion of a photograph, outside the depth of field of a photographic lensing system used to create the image, may be of particular interest. In instances such as these, it is useful to sharpen or improve a definition between edges or boundaries of objects within the image or document. For instance, there can be a desire to improve a distinctiveness or reduce fuzziness at the edges of characters in a text document, between image objects, such as people and real world objects depicted in photographic images, or between elements of a business graphic, such as bars of a bar chart or wedges of a pie chart.
Where image data is available in contone format wherein image pixels describe an input or desired color with one or more values in a relatively broad range of, for example, 0 to 255 (i.e., in 8 bit quantization systems), edge detection algorithms can be used to locate edges of image objects such as the edges or boundaries of test characters, lines, bars and pie wedges, and real world objects. When an image is described in terms of contone values, there is a high probability that sudden pixel-value-to-adjacent-pixel-value changes in the image data are associated with an edge or boundary of an image object. Accordingly, detecting edges of image objects based on the sudden or abrupt pixel value changes can be very reliable. Therefore, in order to enhance the sharpness of image objects represented in terms of contone pixel values, edge detection algorithms can be used to detect the boundaries of image objects and edge or contrast enhancement algorithms can be applied to the image data to enhance the apparent sharpness of the image object edges. For instance, the boundaries of text characters can be identified and pixels within or on the boundaries can be darkened while pixels external to the boundaries can be lightened.
However, image descriptions are not always available in terms of contone values. Sometimes, image descriptions are only available in what is referred to as a binary or other relatively highly quantized format (compared to the quantization used for contone values). Binary image description formats are useful because many marking engines are typically limited to placing a mark or not placing a mark at any given pixel location. Binary image formats describe an image in terms of placing a mark or not placing a mark. Even where pixels are referred to as—high addressable—and describe pixels with more than two (on, off) states of a binary format, ultimately, they describe binary states of a cluster of spots wherein each member of the cluster is either marked or unmarked.
These quantized or binary image descriptions are often generated by applying a halftoning or quantization reduction process to an image described in other terms. Therefore, herein we refer to all binary or highly quantized image descriptions as halftoned. In halftoned images, the lightness, darkness or intensity of colors in areas of an image is associated with a relative spatial density of positive or negative marking decisions. Where quantization reduction is achieved through halftone screening, darkness or lightness or color intensity is associated with the relative size and/or spatial frequency of occurrence of resulting halftone structures, such as, halftone spots, lines or other shapes. In totally saturated (e.g., completely dark) regions of an image, the halftone structures grow to such a size that they meet or blend together and do not pose a particular problem for edge detection algorithms.
However, intermediate shades of gray or highlight colors are associated with halftone structures that are spaced from one another. For example, referring to FIG. 1, an enlarged view 110 of a gray or highlight region of an image includes relatively isolated halftone structures 114. A darker region 118 of the image portion 110 includes relatively large halftone structures, while a relatively light region 126 includes relatively small halftone structures. However, all of the halftone structures include clearly defined boundaries or edges 130.
Each of these boundaries or edges 130 is associated with abrupt changes in pixel values between neighboring pixels. For instance, pixels having pixel values of 1 (or 255) are directly adjacent to pixels having values of 0. Accordingly, edge detection algorithms processing such halftoned images would identify the boundaries of each halftone structure (e.g., 114) as an edge to be processed according to an edge enhancement algorithm.
However, typically, this effect is not desired. Instead, what is desired is to locate the edges or boundaries of large image objects, such as the manmade or naturally occurring objects depicted in a photograph or the lines and text or business graphic elements of non-photographic portions of documents.
Attempts to address this problem and to provide edge enhancement for images where image data is only available in a halftoned, binary or reduced quantization level format include processes referred to as—descreening—halftoned image data and then applying a sharpening technique to the descreened version of the image data. For example, descreening includes spatially low pass filtering the image data thereby causing halftone structures (e.g., 114) to spread out or blend together. Such descreening algorithms are often associated with the side effect of blurring or unsharpening the very image objects for which sharpening is desired. Therefore, descreening the image data that is to be rendered can be counterproductive to the purpose of sharpening the image data and techniques that include descreening and then sharpening the image data to be rendered can yield an image that is still less sharp than one might want.
For example, referring to FIG. 2, an input image 210 is blurry due to some defect that may have occurred prior to halftoning. For instance, a user may have scanned an old blurry image that was halftoned at some point in its history. Descreening, sharpening and rescreening can be applied to the image data of the input image 210. For example, referring to FIG. 3, descreening, sharpening and rescreening algorithms were applied sequentially to the image data of the input image 210 according to a wide range of descreening and sharpening parameters. A prior art output image 310 illustrates the sharpest or best overall image that could be achieved.
Accordingly, there is a desire for systems and methods for producing images with improved sharpness based on halftoned input image data.